# -*- coding: utf-8 -*-

import tensorflow as tf
from tensorflow import keras

import numpy as np

# mnist = tf.keras.datasets.mnist

# (x_train, y_train), (x_test, y_test) = mnist.load_data()
# x_train, x_test = x_train / 255.0, x_test / 255.0

# model = tf.keras.models.Sequential([
#     tf.keras.layers.Flatten(input_shape=(28, 28)),
#     tf.keras.layers.Dense(512, activation=tf.nn.relu),
#     tf.keras.layers.Dropout(0.2),
#     tf.keras.layers.Dense(10, activation=tf.nn.softmax)
# ])
# model.compile(optimizer='adam',
#               loss='sparse_categorical_crossentropy',
#               metrics=['accuracy'])

# model.fit(x_train, y_train, epochs=5)
# model.evaluate(x_test, y_test)

imdb = keras.datasets.imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
